Diversity among individuals and accuracy of individuals are two important factors to decide the ensemble generalization error, whereas enhancing diversity is at the cost of decreasing the accuracy of individuals. Hence, in order to improve the performance of radar target recognition classified by a single classifier, this paper introduces a new radar target recognition method based on the integer matrix linear transformation selective classifier ensemble that considers the balance of diversity and accuracy. Firstly, in order to enhance the diversity, the individual classifiers are considered as original targets of the linear transformation, and instead of the mean value of samples, the true labels are considered to construct an integer matrix. By projecting individual classifiers on the lines through the true labels, a set of new classifiers is obtained based on the project transformation. Secondly, according to two rules that measuring the performance of the classifier, the accuracy rate and RPF-measure, some new classifiers that can obtain better performance are selected to ensemble for increasing the accuracy of classifiers of an ensemble. Finally, the performance of radar target recognition is improved by combining the selected new classifiers. Experimental results of UCI datasets and the radar range profile indicate that the proposed method balances effectively diversity and accuracy, and that it can obtain better performance for radar target recognition compared with single classifier algorithms and other methods.
